Retail stores are in business to sell merchandise and make a profit. Store managers are most concerned with product-related marketing and decisions such as product placement, assortment, space, price, promotion, and inventory. If the products are non-optimized in terms of these product decisions, then sales can be lost and profit will be less than what would otherwise be possible in an optimal system. For example, if the product assortment, space, or inventory is not properly selected or maintained, then the consumer is less likely to buy these products. If price is too high or too low, then profit can be lost. If promotions are not properly targeted, then marketing efforts will be wasted. If the product placement is poorly laid-out, then the store loses sales.
In order to maximize the outcome of product related decisions, retail store management has used statistical modeling and strategic planning to optimize the decision making process for each of the product decisions. Economic modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. An economic-based system will have many variables and influences which determine its behavior. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task, but one that is extremely important and valuable, is to generate a mathematical model of the system which predicts how the system will behave, or would have behaved, with different sets of data and assumptions. The field of probability and statistics has provided many tools which allow predictions to be made with reasonable certainty and acceptable levels of confidence.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve an accurate correlation of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty. As a simple test of the quality of the model, if historical data is processed through the model and the outcome of the model using that historical data is closely aligned with the known historical outcome, then the model is considered to have a high confidence level over the interval. The model should then do a good job of predicting outcomes of the system to different sets of input data.
Economic modeling has many uses and applications. One emerging area in which modeling has exceptional promise is in the retail sales environment. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited customers and business. Most, if not all, retail stores make every effort to maximize sales, volume, revenue, and profit. Economic modeling can be a very effective tool in helping store owners and managers achieve these goals.
Retail stores engage in many different strategies to increase sales volume, revenue, and profit. Retailers must take into account many different considerations in optimizing overall sales volume, revenue, and profit. Product assortment, space, and inventory must be considered. Product price is also important. Product placement in terms of aisle, shelf height, page, and adjacencies must be taken into account. Product promotion is an important factor.
Retailers have used a variety of modeling tools to represent and optimize one or more of the product decisions described above, i.e., product placement, assortment, space, price, promotion, and inventory. One modeling tool may optimize for placement. Another modeling tool will optimize for product assortment, space, and inventory. Yet another modeling tool may optimize for price. Still another modeling tool will predict the optimal promotions. Each modeling tool may yield good results for the specific criteria being considered. However, historical modeling tools generally optimize for only one product decision. The process of optimizing one product decision may not necessarily optimize another product decision. Indeed, optimizing one product decision may be counter-productive to the best solution for another product decision. For example, optimizing product placement, e.g., giving a product a low visibility location, may be counter to optimizing product promotion in that customers may have difficulty finding the advertised product.
By optimizing for only one product decision, or individually for multiple product decisions, then the overall product sales and profit will be sub-optimal. With the present modeling tools, it is difficult, if not impossible, to optimize for all product decisions at once. Either certain product decisions are not considered, or the process of optimizing certain product decisions will detract from other product decisions. In any case, the overall product sales and profit, taking into account all product decisions, is not optimized with present modeling tools.